Connecting Deep Aquifer Recharge in California's Central Valley to Sierra Nevada Snowmelt via Multi-Sensor Remote Sensing Data
Bibliographic record
Abstract
Abstract. California's arid Central Valley (CV) relies on groundwater pumped from deep aquifers (i.e., >50 m) and surface water transported from the Sierra Nevada to produce a quarter of the United States’ food demand. Similar to other basin aquifers adjacent to high mountains, the natural recharge to CV’s deep aquifers is thought to be regulated by the adjacent high mountains of the Sierra Nevada, but the underlying mechanisms remain elusive. We investigate large sets of geodetic remote sensing, hydrologic, and climate data and employ a first-order model assessment at annual time scales to investigate possible recharge mechanisms. Peak annual groundwater storage in the CV lags several months behind groundwater levels, suggesting a longer transmission time for water flow than pressure propagation. We further find that peak groundwater levels lag the Sierra Nevada snowmelt by about one month, consistent with an ideal fluid pressure diffusion time in the Sierra’s fractured crystalline body. Our results suggest that high mountain snowpack changes likely impact freshwater availability in the basin aquifers. Our analysis and a first-order pressure propagation model link the current precipitation and meltwater in the high mountain Sierra to deep CV aquifers through mountain block recharge process, highlighting the importance of longer groundwater flow paths through bedrocks for recharging deep aquifers in CV and other basin aquifer systems adjacent to mountains globally. This underscores the need for new hydroclimate models to fully account for the role of high mountains in lowland water cycles by including mountain block recharge, and revision of current management and drought resiliency plans in California.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".